1. Field of the Invention
The present invention relates generally to systems and methods for call handling, and more particularly to quality of service management within a call handling system.
2. Discussion of Background Art
Automated call handling systems, such as Interactive Voice Response (IVR) systems, using Automatic Speech Recognition (ASR) and Text-to-speech (TTS) software are increasingly important tools for providing information and services to contacts, such as customers, in a more cost efficient manner. IVR systems are typically hosted by call centers that enable contacts to interact with corporate databases and services over a telephone using a combination of voice speech signals and telephone button presses. IVR systems are particularly cost effective when a large number of contacts require data or services that are very similar in nature, such as banking account checking, ticket reservations, etc., and thus can be handled in an automated manner often providing a substantial cost savings due to a need for fewer human operators.
The IVR systems ability to recognize a customer's speech signals is a major factor in the customer's perception of the quality of service provided by the call center. Due to the importance of understanding one's customers, many call center's attempt to provide a very robust set of ASR classifiers for interpreting the customer's speech signals. However in order to implement such a robust ASR system, call centers must invest in a substantial amount of hardware resources as well as a complex, time consuming set of ASR classifiers. The costs of implementing such a system may however be so high as to make such an IVR system impractical for some companies and service providers. Such providers may then need to resort to either an inferior set of ASR tools or a greater number of human operators.
Some call centers attempt to balance the ASR classifier costs with the benefits of having an IVR system by selecting a predetermined number of ASR classifiers for interpreting the contact's speech signals that fall within the call center's current budget. However, such monolithic approaches can in some implementations result in an inordinate number of speech recognition errors for hard to understand contacts who not only are disappointed with the call center experience, but who also must be shifted over to more costly human operators in order to complete the call. In other implementations, such a monolithic approach can result in a much higher per caller cost, since many callers could have been understood using just one ASR classifier, but instead the call center used a much larger number of ASR classifiers resulting in a greater fixed cost to the call center.
Other call centers attempt to balance the ASR classifier costs with the benefits of having an IVR system by using a greater number of ASR classifiers to understand the contact based on the availability of hardware resources, such as processor power and available memory, within the call center. Such a system thus would tend to provide a degraded quality of service level during peak calling times, and perhaps would only provide an acceptable quality of service level during slow times.
In response to the concerns discussed above, what is needed is a system and method for quality of service management that overcomes the problems of the prior art.